size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9944334
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] -0.1176408 -0.0839535 -0.1816618
[2,] -1.9118626 -1.9007010 -1.9725283
[3,] 0.5285488 0.3210765 0.4230086
[4,] 0.9836280 1.0358161 1.0100268
[5,] -0.0167236 -0.1766083 -0.1148486
[6,] -0.5475162 -0.7176660 -0.8387611
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.983 -0.289 0.984 -0.211 -0.292 0.985
y3 0.983 1.000 -0.297 0.986 -0.235 -0.305 0.983
x2 -0.289 -0.297 1.000 -0.299 0.987 0.992 -0.261
y1 0.984 0.986 -0.299 1.000 -0.231 -0.306 0.987
x1 -0.211 -0.235 0.987 -0.231 1.000 0.990 -0.200
x3 -0.292 -0.305 0.992 -0.306 0.990 1.000 -0.272
y2 0.985 0.983 -0.261 0.987 -0.200 -0.272 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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